Short-term Traffic Flow Combination Forecasting Based on Entropy Approac
Chen Bo · Jisuanji fangzhen · 2013
Study short-term traffic flow forecasting.In the traditional nonparametric regression prediction model,the input variables are selected from the prediction point or adjacent points independently.To improve the accuracy of short-term traffic flow forecasting,a combined forecasting method based on nonparametric regression was put forward.Specifically,two single forecasts used grey correlation degree and correlation coefficient respectively to dynamically select all input variables with error feedback to tune the system parameters and the input variables.Finally,the results of those two forecasting methods were combined together in which entropy theory was utilized to determine the weight of each single forecasting method.The simulation with highway traffic data demonstrates that the proposed combined forecasting method can effectively improve the forecasting accuracy.